third_party/hexagon/svdf_int8.cc

/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

/* Copyright 2020 The Qualcomm Innovation Center, Inc. All Rights Reserved.

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==============================================================================*/

#include <math.h>

#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/op_macros.h"
#include "tensorflow/lite/micro/kernels/activation_utils.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
#include "tensorflow/lite/micro/micro_utils.h"
#include "third_party/hexagon/hexagon_svdf.h"
#include "third_party/hexagon/hexagon_tflm_translation_svdf.h"

namespace tflite {

TfLiteStatus HexagonSvdfEvalInt8(TfLiteContext* context, TfLiteNode* node) {
  auto* params = reinterpret_cast<TfLiteSVDFParams*>(node->builtin_data);
  TFLITE_DCHECK(node->user_data != nullptr);
  const HexagonOpDataSvdf& data =
      *(static_cast<const HexagonOpDataSvdf*>(node->user_data));

  const TfLiteEvalTensor* input =
      tflite::micro::GetEvalInput(context, node, kSvdfInputTensor);
  const TfLiteEvalTensor* weights_feature =
      tflite::micro::GetEvalInput(context, node, kSvdfWeightsFeatureTensor);
  const TfLiteEvalTensor* weights_time =
      tflite::micro::GetEvalInput(context, node, kSvdfWeightsTimeTensor);
  const TfLiteEvalTensor* bias =
      (NumInputs(node) == 5)
          ? tflite::micro::GetEvalInput(context, node, kSvdfBiasTensor)
          : nullptr;
  TfLiteEvalTensor* activation_state = tflite::micro::GetMutableEvalInput(
      context, node, kSvdfInputActivationStateTensor);
  TfLiteEvalTensor* output =
      tflite::micro::GetEvalOutput(context, node, kSvdfOutputTensor);

  if (tflite::hexagon_svdf::HexagonOptimizable(context, node)) {
    tflite::hexagon_svdf::HexagonEvalIntegerSVDF(
        context, node, input, weights_feature, weights_time, bias, params,
        activation_state, output, node->user_data);
  } else {
    EvalInt16SvdfReference(context, node, input, weights_feature, weights_time,
                           bias, params, activation_state, output,
                           data.reference_op_data);
  }
  return kTfLiteOk;
}

void* HexagonSvdfInit(TfLiteContext* context, const char* buffer,
                      size_t length) {
  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
  void* data = context->AllocatePersistentBuffer(context, sizeof(OpDataSvdf));

  if (data == nullptr) {
    return nullptr;
  }

  HexagonOpDataSvdf* opdata = static_cast<HexagonOpDataSvdf*>(data);
  opdata->hexagon_data =
      tflite::hexagon_svdf::HexagonInit(context, buffer, length);

  return data;
}

TfLiteStatus HexagonSvdfPrepare(TfLiteContext* context, TfLiteNode* node) {
  TfLiteStatus prepare_status = PrepareSvdf(context, node);
  if (prepare_status != kTfLiteOk) {
    return prepare_status;
  }

  tflite::hexagon_svdf::HexagonOptimizationEvaluation(context, node);

  if (tflite::hexagon_svdf::HexagonOptimizable(context, node)) {
    TF_LITE_ENSURE_OK(context,
                      tflite::hexagon_svdf::HexagonPrepare(context, node));
  }

  return kTfLiteOk;
}

TFLMRegistration Register_SVDF_INT8() {
  return tflite::micro::RegisterOp(HexagonSvdfInit, HexagonSvdfPrepare,
                                   HexagonSvdfEvalInt8);
}

}  // namespace tflite